{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIIWqgaQoqPR",
        "outputId": "9b109a85-825a-489e-85b4-42a9801a7a5e"
      },
      "outputs": [
        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'google.colab'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m drive\n\u001b[0;32m      2\u001b[0m drive\u001b[38;5;241m.\u001b[39mmount(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/content/gdrive\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
            "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
          ]
        }
      ],
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "xeUuhTurozgJ"
      },
      "outputs": [],
      "source": [
        "# from keras.layers import Input, Lambda, Dense, Flatten\n",
        "# from keras.models import Model\n",
        "# from keras.applications.mobilenet import MobileNet\n",
        "# from keras.applications.resnet50 import ResNet50\n",
        "# from keras.applications.vgg16 import preprocess_input\n",
        "# from keras.preprocessing import image\n",
        "# from keras.preprocessing.image import ImageDataGenerator\n",
        "# from keras.models import Sequential\n",
        "# import numpy as np\n",
        "# from glob import glob\n",
        "# import matplotlib.pyplot as plt\n",
        "from modules import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CfFMMlyKozip"
      },
      "outputs": [],
      "source": [
        "# re-size all the images to this\n",
        "IMAGE_SIZE = [224, 224]\n",
        "\n",
        "# train_path = '/content/gdrive/My Drive/CBIS-DDSM final train_test mammogram data/train'\n",
        "# valid_path = '/content/gdrive/My Drive/CBIS-DDSM final train_test mammogram data/test'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWnxYsZKozlF",
        "outputId": "a178889e-0cce-41a3-b12a-1360f92e768b"
      },
      "outputs": [],
      "source": [
        "# add preprocessing layer to the front of VGG\n",
        "mobile = MobileNet(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
        "\n",
        "# don't train existing weights\n",
        "for layer in mobile.layers:\n",
        "  layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGgNMO8Qoznc",
        "outputId": "20c60e2b-e630-4f59-8e9b-9a1cc86cf8e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n",
            "['D:\\\\cmmd_data\\\\PNG\\\\TRAIN\\\\benign', 'D:\\\\cmmd_data\\\\PNG\\\\TRAIN\\\\malignant']\n"
          ]
        }
      ],
      "source": [
        "# useful for getting number of classes\n",
        "from glob import glob\n",
        "folders = glob(\"D:\\\\cmmd_data\\\\PNG\\\\TRAIN\\\\*\")\n",
        "print(len(folders))\n",
        "print(folders)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "nxg8sTeqozp6"
      },
      "outputs": [],
      "source": [
        "# our layers - you can add more if you want\n",
        "x = Flatten()(mobile.output)\n",
        "# x = Dense(1000, activation='relu')(x)\n",
        "prediction = Dense(len(folders), activation='softmax')(x)\n",
        "\n",
        "# create a model object\n",
        "model = Model(inputs=mobile.input, outputs=prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AaR4IGlozuk",
        "outputId": "a7f730fa-d202-4ccd-d182-d754a492eee0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv1_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_1_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_1_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">113</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">113</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_2_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_2_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,192</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_2_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_2_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_3_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_3_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,384</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_3_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_3_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">57</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">57</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_4_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_4_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,768</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_4_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_4_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_5_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_5_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,536</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_5_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_5_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_6_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_6_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,072</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_6_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_6_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_7_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_7_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_7_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_7_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_8_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_8_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_8_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_8_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_9_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_9_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_9_bn                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_9_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_10_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_10_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_10_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_10_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_11_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_11_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_11_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_11_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_12_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_12_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,288</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_12_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_12_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,216</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_13_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_13_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,048,576</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_13_bn                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_13_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50176</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │       <span style=\"color: #00af00; text-decoration-color: #00af00\">100,354</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv1 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m864\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv1_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m128\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv1_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_1 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m288\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_1_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_1_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_1 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_1_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_1_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_2 (\u001b[38;5;33mZeroPadding2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_2 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m576\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_2_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_2_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_2 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │         \u001b[38;5;34m8,192\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_2_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_2_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_3 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │         \u001b[38;5;34m1,152\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_3_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_3_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_3 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m16,384\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_3_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_3_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_4 (\u001b[38;5;33mZeroPadding2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_4 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │         \u001b[38;5;34m1,152\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_4_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_4_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_4 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m32,768\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_4_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_4_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_5 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m2,304\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_5_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_5_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_5 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │        \u001b[38;5;34m65,536\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_5_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_5_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_6 (\u001b[38;5;33mZeroPadding2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_6 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m2,304\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_6_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_6_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_6 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │       \u001b[38;5;34m131,072\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_6_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_6_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_7 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m4,608\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_7_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_7_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_7 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │       \u001b[38;5;34m262,144\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_7_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_7_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_8 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m4,608\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_8_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_8_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_8 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │       \u001b[38;5;34m262,144\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_8_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_8_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_9 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m4,608\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_9_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_9_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_9 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │       \u001b[38;5;34m262,144\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_9_bn                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_9_relu (\u001b[38;5;33mReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_10 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m4,608\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_10_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_10_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_10 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │       \u001b[38;5;34m262,144\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_10_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_10_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_11 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m4,608\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_11_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_11_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_11 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │       \u001b[38;5;34m262,144\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_11_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_11_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pad_12 (\u001b[38;5;33mZeroPadding2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_12 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │         \u001b[38;5;34m4,608\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_12_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │         \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_12_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_12 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │       \u001b[38;5;34m524,288\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_12_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │         \u001b[38;5;34m4,096\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_12_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_13 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │         \u001b[38;5;34m9,216\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_13_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │         \u001b[38;5;34m4,096\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_dw_13_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_13 (\u001b[38;5;33mConv2D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │     \u001b[38;5;34m1,048,576\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_13_bn                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │         \u001b[38;5;34m4,096\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv_pw_13_relu (\u001b[38;5;33mReLU\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50176\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │       \u001b[38;5;34m100,354\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,329,218</span> (12.70 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,329,218\u001b[0m (12.70 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">100,354</span> (392.01 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m100,354\u001b[0m (392.01 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,228,864</span> (12.32 MB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,228,864\u001b[0m (12.32 MB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "hUSewN8_ozxP"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "opt = tf.keras.optimizers.SGD(learning_rate=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2HVGWEljozzr"
      },
      "outputs": [],
      "source": [
        "# tell the model what cost and optimization method to use\n",
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swZx0hrnoz7G",
        "outputId": "e9c137f0-c71f-480b-aa6f-d52aea3f1143"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2392 images belonging to 2 classes.\n",
            "Found 752 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "training_set = train_datagen.flow_from_directory(\"D:\\\\cmmd_data\\\\PNG\\\\TRAIN\",\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical')\n",
        "\n",
        "test_set = test_datagen.flow_from_directory(\"D:\\\\cmmd_data\\\\PNG\\\\TEST\",\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'categorical')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vQcOIovoz_x",
        "outputId": "a80f0630-2e6f-4fd2-e361-7dad3f170737"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "d:\\Ananocnda\\envs\\Digital_image_processing\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 37s/step - accuracy: 0.5000 - loss: 6.7742 - val_accuracy: 0.7261 - val_loss: 2.7448\n",
            "Epoch 2/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 29s/step - accuracy: 0.5312 - loss: 4.5408 - val_accuracy: 0.7021 - val_loss: 9.6161\n",
            "Epoch 3/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 26s/step - accuracy: 0.7083 - loss: 10.9312 - val_accuracy: 0.6144 - val_loss: 9.2495\n",
            "Epoch 4/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.7396 - loss: 7.8960 - val_accuracy: 0.4189 - val_loss: 11.4110\n",
            "Epoch 5/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 23s/step - accuracy: 0.5312 - loss: 9.7834 - val_accuracy: 0.5053 - val_loss: 5.4140\n",
            "Epoch 6/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 25s/step - accuracy: 0.4583 - loss: 4.2480 - val_accuracy: 0.7340 - val_loss: 3.0719\n",
            "Epoch 7/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 23s/step - accuracy: 0.5625 - loss: 4.2854 - val_accuracy: 0.7513 - val_loss: 3.1462\n",
            "Epoch 8/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 23s/step - accuracy: 0.6042 - loss: 4.1193 - val_accuracy: 0.6769 - val_loss: 3.0405\n",
            "Epoch 9/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 24s/step - accuracy: 0.7396 - loss: 2.2067 - val_accuracy: 0.4495 - val_loss: 5.6179\n",
            "Epoch 10/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 26s/step - accuracy: 0.6562 - loss: 4.9617 - val_accuracy: 0.4814 - val_loss: 4.8044\n",
            "Epoch 11/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 27s/step - accuracy: 0.6250 - loss: 3.2619 - val_accuracy: 0.6928 - val_loss: 2.8422\n",
            "Epoch 12/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 24s/step - accuracy: 0.6875 - loss: 3.2375 - val_accuracy: 0.7247 - val_loss: 4.3217\n",
            "Epoch 13/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.6146 - loss: 4.5956 - val_accuracy: 0.7234 - val_loss: 4.7926\n",
            "Epoch 14/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.6562 - loss: 6.4996 - val_accuracy: 0.6995 - val_loss: 4.3787\n",
            "Epoch 15/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 24s/step - accuracy: 0.6875 - loss: 4.1585 - val_accuracy: 0.4973 - val_loss: 4.4411\n",
            "Epoch 16/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 24s/step - accuracy: 0.5729 - loss: 4.5944 - val_accuracy: 0.4069 - val_loss: 5.0622\n",
            "Epoch 17/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 24s/step - accuracy: 0.4896 - loss: 4.3889 - val_accuracy: 0.7141 - val_loss: 2.6821\n",
            "Epoch 18/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.6354 - loss: 2.6568 - val_accuracy: 0.7247 - val_loss: 4.3347\n",
            "Epoch 19/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.7292 - loss: 3.6428 - val_accuracy: 0.7287 - val_loss: 4.0804\n",
            "Epoch 20/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.7396 - loss: 3.5041 - val_accuracy: 0.7114 - val_loss: 2.5730\n",
            "Epoch 21/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.5417 - loss: 4.0506 - val_accuracy: 0.5013 - val_loss: 3.7643\n",
            "Epoch 22/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.6354 - loss: 2.7633 - val_accuracy: 0.4668 - val_loss: 4.2644\n",
            "Epoch 23/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 23s/step - accuracy: 0.6771 - loss: 2.8079 - val_accuracy: 0.6769 - val_loss: 2.7490\n",
            "Epoch 24/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 24s/step - accuracy: 0.6667 - loss: 3.3630 - val_accuracy: 0.7261 - val_loss: 3.3765\n",
            "Epoch 25/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 24s/step - accuracy: 0.5833 - loss: 5.2844 - val_accuracy: 0.7234 - val_loss: 2.3570\n",
            "Epoch 26/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 24s/step - accuracy: 0.7188 - loss: 2.2167 - val_accuracy: 0.5878 - val_loss: 2.3412\n",
            "Epoch 27/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 26s/step - accuracy: 0.6667 - loss: 1.6100 - val_accuracy: 0.5359 - val_loss: 3.1312\n",
            "Epoch 28/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 23s/step - accuracy: 0.5729 - loss: 3.5710 - val_accuracy: 0.6702 - val_loss: 2.1955\n",
            "Epoch 29/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 25s/step - accuracy: 0.6771 - loss: 1.7739 - val_accuracy: 0.7088 - val_loss: 2.3570\n",
            "Epoch 30/30\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 24s/step - accuracy: 0.6349 - loss: 2.7820 - val_accuracy: 0.6928 - val_loss: 2.2044\n"
          ]
        }
      ],
      "source": [
        "# fit the model\n",
        "r = model.fit(\n",
        "  training_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=30,\n",
        "  steps_per_epoch=int(len(training_set)/32),\n",
        "  validation_steps=int(len(test_set)/32)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')\n",
        "\n",
        "# accuracies\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "QfbX-iQZo0Cm",
        "outputId": "90a90c24-a441-42ae-917b-775f6ac9f4e3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')\n",
        "\n",
        "# accuracies\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "kP3xBrSMo0Es"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(test_set).argmax( axis=-1 ) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "09CA-pCro0IL",
        "outputId": "4400e270-162c-4372-996b-9d8e60e2b34a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "752\n",
            "752\n"
          ]
        }
      ],
      "source": [
        "print(len(predictions))\n",
        "# predictions.resize(357,refcheck=False)\n",
        "print(len(predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "id": "oQ600A3foz92",
        "outputId": "26cbfd88-e36b-418b-961b-6a5831fc9e63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1\n",
            " 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1\n",
            " 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0\n",
            " 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 0\n",
            " 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1\n",
            " 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1\n",
            " 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0\n",
            " 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 1\n",
            " 0 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1\n",
            " 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0 0 1 1 1 1\n",
            " 1 1 1 0 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1\n",
            " 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1\n",
            " 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 0 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 1 0\n",
            " 0 1 1 1 1 1 1 0 1 1 0 0]\n"
          ]
        }
      ],
      "source": [
        "print ( predictions )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "YaJYYg8Toz4n",
        "outputId": "1e340a8c-c9f0-49f9-d980-709f28e6a2f1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "F1_score :  0.6050531914893617\n",
            "sensitibity :  0.6050531914893617\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.25      0.17      0.20       224\n",
            "           1       0.69      0.79      0.74       528\n",
            "\n",
            "    accuracy                           0.61       752\n",
            "   macro avg       0.47      0.48      0.47       752\n",
            "weighted avg       0.56      0.61      0.58       752\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "#print(test_generator)\n",
        "print('F1_score : ',f1_score(test_set.classes,predictions,average='micro'))\n",
        "print('sensitibity : ',metrics.recall_score(test_set.classes,predictions,average='micro'))\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_set.classes, predictions))\n",
        "# from sklearn.metrics import f1_score\n",
        "# from sklearn import metrics\n",
        "# # from sklearn.metrics import plot_confusion_matrix\n",
        "# #print(test_set)\n",
        "# print('F1_score : ',f1_score(test_set.classes,predictions,average='micro'))\n",
        "# print('sensitibity : ',metrics.recall_score(test_set.classes,predictions,average='micro'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "根据你提供的报告，我们可以得到以下信息：\n",
        "1. **F1分数**：\n",
        "   - F1分数为0.605，这是一个介于0和1之间的数值，表示模型的整体性能。F1分数接近1表示模型性能很好，接近0则表示性能较差。这个分数表明模型在一定程度上是有效的，但还有改进空间。\n",
        "2. **敏感度（召回率）**：\n",
        "   - 敏感度也为0.605，这与F1分数相同。这意味着模型能够正确识别出大约60.5%的正类样本。\n",
        "3. **分类报告**：\n",
        "   - 报告中提到了两个类别（0和1）的精确率、召回率和F1分数，以及这些指标的平均值。\n",
        "   - 类别1的精确率为0.69，召回率为0.79，F1分数为0.74，这表明模型在类别1上的性能较好。\n",
        "   - 类别0的精确率为0.25，召回率为0.17，F1分数为0.20，这表明模型在类别0上的性能较差，有很大的提升空间。\n",
        "4. **总体准确率**：\n",
        "   - 模型的总体准确率为0.61，即模型正确预测的样本占总样本的61%。\n",
        "5. **宏平均和加权平均**：\n",
        "   - 宏平均（macro avg）是指简单地对每个类别的指标取平均，不考虑每个类别的样本量。\n",
        "   - 加权平均（weighted avg）则是根据每个类别的支持度（即样本量）对指标进行加权平,更贴近实际情况的整体性能指标。\n",
        "总结来说，模型在类别1(恶性)上的性能较好，但在类别0(良性)上的性能较差。整体而言，模型的准确率和F1分数表明它有一定的预测能力，但还有改进的余地，特别是在类别0上。可能需要进一步分析模型在类别0上的错误类型，以便进行针对性的改进。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "PD-Ot4O7oz2b",
        "outputId": "9e51e1c0-ac40-46c9-abaf-d352ec49e997"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "Found input variables with inconsistent numbers of samples: [752, 357]",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[21], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m classification_report\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mclassification_report\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_set\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclasses\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m)\u001b[49m)\n",
            "File \u001b[1;32md:\\Ananocnda\\envs\\Digital_image_processing\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
            "File \u001b[1;32md:\\Ananocnda\\envs\\Digital_image_processing\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:2604\u001b[0m, in \u001b[0;36mclassification_report\u001b[1;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[0;32m   2469\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[0;32m   2470\u001b[0m     {\n\u001b[0;32m   2471\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2495\u001b[0m     zero_division\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwarn\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   2496\u001b[0m ):\n\u001b[0;32m   2497\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Build a text report showing the main classification metrics.\u001b[39;00m\n\u001b[0;32m   2498\u001b[0m \n\u001b[0;32m   2499\u001b[0m \u001b[38;5;124;03m    Read more in the :ref:`User Guide <classification_report>`.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2601\u001b[0m \u001b[38;5;124;03m    <BLANKLINE>\u001b[39;00m\n\u001b[0;32m   2602\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2604\u001b[0m     y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2606\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2607\u001b[0m         labels \u001b[38;5;241m=\u001b[39m unique_labels(y_true, y_pred)\n",
            "File \u001b[1;32md:\\Ananocnda\\envs\\Digital_image_processing\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:85\u001b[0m, in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_targets\u001b[39m(y_true, y_pred):\n\u001b[0;32m     59\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[0;32m     60\u001b[0m \n\u001b[0;32m     61\u001b[0m \u001b[38;5;124;03m    This converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[38;5;124;03m    y_pred : array or indicator matrix\u001b[39;00m\n\u001b[0;32m     84\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 85\u001b[0m     \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     86\u001b[0m     type_true \u001b[38;5;241m=\u001b[39m type_of_target(y_true, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     87\u001b[0m     type_pred \u001b[38;5;241m=\u001b[39m type_of_target(y_pred, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_pred\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[1;32md:\\Ananocnda\\envs\\Digital_image_processing\\Lib\\site-packages\\sklearn\\utils\\validation.py:457\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    455\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[0;32m    456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 457\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    458\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    459\u001b[0m         \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[0;32m    460\u001b[0m     )\n",
            "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [752, 357]"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_set.classes, predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fyEFLrjnozs0"
      },
      "outputs": [],
      "source": [
        "def plot_roc_curve(fpr,tpr): \n",
        "  plt.plot(fpr,tpr) \n",
        "  plt.axis([0,1,0,1]) \n",
        "  plt.xlabel('False Positive Rate') \n",
        "  plt.ylabel('True Positive Rate') \n",
        "  plt.show() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "-kPGN0w7ozd9",
        "outputId": "6bcad310-a822-438a-cd75-676af4b0c40d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.47876647834274955\n",
            "-0.042525813862399404\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve,roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "\n",
        "fpr , tpr , thresholds = roc_curve (test_set.classes,predictions)\n",
        "plot_roc_curve (fpr,tpr)\n",
        "auc_score=roc_auc_score(test_set.classes,predictions) \n",
        "print(auc_score) \n",
        "print(matthews_corrcoef(test_set.classes,predictions))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "MobileNet using CBIS_DDSM.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
